scholarly journals Aquifer parameter and zonation structure estimation using meshless local Petrov–Galerkin method and particle swarm optimization

2017 ◽  
Vol 20 (2) ◽  
pp. 457-467 ◽  
Author(s):  
Boddula Swathi ◽  
T. I. Eldho
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Measurement Errors ◽  
Particle Swarm ◽  
Least Square ◽  
Suitable Model ◽  
Swarm Optimization ◽  
Aquifer Parameter ◽  
Marquardt Algorithm ◽  
Model Structures

Abstract The simulation-optimization (SO) modeling approach can be effectively used for aquifer parameter estimation. In this study, a numerical approach based on meshless local Petrov–Galerkin (MLPG) method is used for groundwater flow simulation and coupled with particle swarm optimization model for optimization. The study deals with the identification of the most suitable model structure for a hypothetical heterogeneous confined aquifer from a number of alternate models using zonation method of parameter estimation. A range of alternate models starting from homogeneous to more complex model structures are considered for the zonation. Inverse modeling of different model structures is carried out based on weighted least square performance criterion. The suitable models are selected and reliability analysis ascertained by computing three parameters of composite scaled sensitivity, coefficient of variation, and confidence interval, and the best model is selected. Sensitivity of estimated parameters is investigated by considering different sets of head data involving possible measurement errors. The solutions are compared with another inverse model using the MLPG and Levenberg–Marquardt algorithm. Based on the results, it is found that the proposed methodology can be utilized in the estimation of different unknown parameters in a regional groundwater system.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

2014 ◽  
Vol 511-512 ◽  
pp. 927-930
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Jin Huang ◽  
He Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Prediction ◽  
Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Grain Temperature-Dependent Thermal Properties Estimation Using FI-QPSO Algorithm

2021 ◽  
Author(s):  
Hongmei Xu ◽  
Juan Liu ◽  
Kun Wang ◽  
Songtao Kong ◽  
Yong Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Properties ◽  
Measurement Errors ◽  
Fuzzy Inference ◽  
Quantum Particle ◽  
Estimation Method ◽  
Particle Swarm ◽  
Temperature Dependent ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Abstract A hybrid fuzzy inference-quantum particle swarm optimization (FI-QPSO) algorithm is developed to estimate the temperature-dependent thermal properties of grain. The fuzzy inference scheme is established to determine the contraction-expansion coefficient according to the aggregation degree of particles. The heat transfer process in the grain bulk is solved using the finite element method (FEM), and the estimation task is formulated as an inverse problem. Numerical experiments are performed to study the effects of the surface heat flux, number of measurement points, measurement errors and the individual space on the estimation results. Comparison with the quantum particle swarm optimization (QPSO) algorithm and conjugate gradient method (CGM) is also conducted, and it shows the validity of the estimation method established in this paper.

scholarly journals Employing particle swarm optimization algorithm for shrinkage parameter estimation in generalized Liu estimator

2020 ◽  
Vol 8 (1) ◽  
pp. 10
Author(s):  
Qamar Abdulkareem Abdulazeez ◽  
Zakariya Yahya Algamal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Method ◽  
Least Square ◽  
Superior Performance ◽  
Ridge Estimator ◽  
Swarm Optimization ◽  
Liu Estimator ◽  
Ordinary Least Square ◽  
Parameter Matrix

It is well-known that in the presence of multicollinearity, the Liu estimator is an alternative to the ordinary least square (OLS) estimator and the ridge estimator. Generalized Liu estimator (GLE) is a generalization of the Liu estimator. However, the efficiency of GLE depends on appropriately choosing the shrinkage parameter matrix which is involved in the GLE. In this paper, a particle swarm optimization method, which is a metaheuristic continuous algorithm, is proposed to estimate the shrinkage parameter matrix. The simulation study and real application results show the superior performance of the proposed method in terms of prediction error.   

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